Nonparametric estimation of sponsored search auctions and impacts of ad quality on search revenue

This paper presents an empirical model of sponsored search auctions in which advertisers are ranked by bid and ad quality. We introduce a new nonparametric estimator for the advertiser's ad value and its distribution under the 'incomplete information' assumption. The ad value is characterized by a tractable analytical solution given observed auction parameters. Using Yahoo! search auction data, we estimate value distributions and study the bidding behavior across product categories. We find that advertisers shade their bids more when facing less competition. We also conduct counterfactual analysis to evaluate the impact of score squashing (ad quality raised to power &#x03b8; < 1) on the auctioneer's revenue. Our results show that product-specific score squashing can enhance auctioneer revenue at the expense of advertiser profit and consumer welfare.